Can data analysis using software provide rigor?
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Can data analysis using software provide rigor?

As with the use of another type of tool, such as a word processor, which allows us to present articles and reports using different formats (images, tables, graphs), fonts, organization of information, etc., CAQDAS have become increasingly robust. However, using a CAQDAS that works online or offline does not necessarily confer quality to the work carried out by the researcher. The rigor and systematization of research work largely depend on the human vector. A word processor does not write alone either, although there are already voice recognition and audio transcription functionalities.

Even if a CAQDAS is used, the researcher remains one of the instruments of data collection. The care taken in the different phases of validation procedures makes it possible to reduce or minimize the bias often associated with qualitative studies, often seen as a problem but nevertheless a characteristic of this type of approach.

The most relevant aspect of using a CAQDAS that works online is the possibility of working collaboratively, synchronously, or asynchronously. About webQDA, some studies were carried out, such as: “Collaborative Work: real-time coding” (Costa, 2020), “Collaborative Work in Qualitative Research through Technologies” (Costa, Neri de Souza & Neri de Souza, 2106), “Collaborative work supported by technologies: the example of qualitative research” (Costa & Costa, 2017), “Functionalities for the Promotion of Collaborative Work in Qualitative Research: The webQDA software case” (Costa, Neri de Souza, Reis & Freitas, 2016).

At the same time, the exploration of tools that allow to check or assess the quality of scientific work, essentially articles, is something increasingly used in the research phases. Tools such as the Qualitative Research Evaluation Tool – QRe Tool (Costa & Minayo, 2019), Consolidated Criteria for Reporting Qualitative Studies – COREQ (Booth et. al., 2014), and the Enhancing transparency in reporting the synthesis of qualitative research – ENTREQ (Tong et. al., 2012), in their checklists, question the number of researchers (coders) involved. These tools assume that collaborative or cooperative work, for example, in the coding process, involves more than one researcher.

Since its inception in 2010, webQDA software has continued to focus on developing features that promote collaborative work, which we designate as collaborative research. The fact that this tool works in the cloud enhances collaboration at different stages of a research project. Figure 1 shows the functionality that allows inviting other users. There are two user profiles (guest and collaborator) that can be invited to a project. The main difference between the two profiles is that the guest user can only interact in two functions: insert comments and logbook entries. In the case of the collaborating user, it is possible to allow them to encode, but we can activate the option not to allow decoding. For example, we can allow them to encode, and then not allow them to change the work done so that the project manager analyses the coding. Regarding data protection, it allows the manager to hide a specific user from other users. At any time, it is possible to block a user, change the profile and transfer the project management to another user (provided that they are a collaborator).

Figure 1 – Users’ management in webQDA

Figure 2 shows the encodings of different users. These codifications are carried out in real-time. For example, if there are 3 coders in the project and they are analyzing 10 interviews, they can define as coding strategy:

  • Two of the users code 5 interviews each;
  • Later, the users invert the roles and validate the other user’s coding;
  • The third user randomly selects 10% of each interview and validates what the other two users carried out.
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General tips for encoding Qualitative Data

One of the main errors verified in research is the lack of planning of adequate methods for data analysis. For example, to develop a data collection instrument, it is necessary to pay attention to the tools used to obtain results (analysis). Analysing qualitative data is not a task without difficulties, as the non-numeric and unstructured data corpus is generally diffuse and complex. There are no clear and widely accepted rules on how to analyse non-numeric and unstructured data.
The seven essential steps or subtasks that we will describe below are transversal or generic to qualitative data analysis techniques. The technique’s focus on the analysis rests on specific choices according to each objective and research questions.
For very practical purposes, it can be said that qualitative research of scientific nature has three stages: (1) an exploratory phase; (2) fieldwork; (3) analysis and treatment of the empirical and documentary material. The exploratory phase consists of the production of the research project and all the procedures necessary for preparation to enter the field. The fieldwork phase constitutes the primordial moment for understanding, in intersubjectivity, the empirical reality under study.